Angewandte Datenverarbeitung und Visualisierung (WiSe23/24)
  • D. Palleschi
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  1. Overview
  2. Syllabus
  • Overview
    • Preface
    • Syllabus
    • Kursübersicht
  • Course mats
    • 1  Intro to R
  • Reports
    • 2  Bericht 1
    • 3  Bericht 2
    • 4  Bericht 3:
  • References

Syllabus

The main aim of this course is to develop the knowledge and skills required to implement an “Exploratory Data Analysis (EDA)”. EDA is not a formal process with specific rules, but is rather “a state of mind” (Wickham et al., o. J., Ch. 11). The knowledge required to carry out an EDA is simply understanding your data and how to explore its structure to get an understanding for its distribution and patterns. The skills required to carry out an EDA are specific to the language used to conduct your EDA, which in our case is R.

Woche Datum Thema Vorbereitung
1 2023-10-18

Intro to R

[📚 R4DS - Ch 1 (Introduction)
📚 R4DS - Ch 3 (Workflow: Basics)](https://r4ds.hadley.nz/intro
https://r4ds.hadley.nz/workflow-basics)

2 2023-10-25

Data Viz 1: Distributions and comparing groups

R4DS - Ch. 1 (Introduction)
📚 R4DS - Ch. 3 (Workflow: Basics)

3 2023-11-01

Wrangling 1: Transforming data

📚 R4DS - Chp 6

4 2023-11-08

Dynamic reports with Quarto

📚 ggplot2-book - Chp 5

5 2023-11-15

Regular Expressions

📚 R4DS - Chp 21

6 2023-11-22

Bericht 1

📚 fdv - Chp 14

7 2023-11-29

Descriptive Stats

📚 fdv - Chp 29

8 2023-12-06

Reading in data

📃 Six part series on W.E.B. Du Bois’ legacy

9 2023-12-13

Wrangling 2: Tidying data

📚 fdv - Chp 10

10 2023-12-20

Data Viz 2: Visual summaries

Vorlesungsfrei 2023-12-27

☃️

🎿

Vorlesungsfrei 2024-01-03

☃️

🎿

11 2024-01-10

Bericht 2

📃 The case against diverging stacked bars
📃 On Likert Scales In R

12 2024-01-17

Writing functions

📃 sf vgnette 1 + 📃 sf vgnette 2 +
📃 sf vgnette 3 + 📃 sf vgnette 4 +
📃 sf vgnette 5

13 2024-01-24

Inferential statistics

📚 fdv - Chp 19
📚 fdv - Chp 4

14 2024-01-31

Data Viz 3: visualising your model

📃 gganimate vignette

15 2024-02-07

Bericht 3

16 2024-02-14

Open session: Q&A

Wickham, H., Çetinkaya-Rundel, M., & Grolemund, G. (o. J.). R for Data Science (2. Aufl.). https://r4ds.hadley.nz/
Preface
Kursübersicht
Quellcode
---
lang: de
execute:
  echo: false
bibliography: references.bib
csl: apa.csl
---

```{r, eval = T, cache = F}
rbbt::bbt_update_bib("syllabus.qmd")
```

# Syllabus {.unnumbered}

The main aim of this course is to develop the knowledge and skills required to implement an "Exploratory Data Analysis (EDA)". EDA is not a formal process with specific rules, but is rather "a state of mind" [@wickham_r_nodate, Ch. 11]. The knowledge required to carry out an EDA is simply understanding your data and how to explore its structure to get an understanding for its distribution and patterns. The skills required to carry out an EDA are specific to the language used to conduct your EDA, which in our case is R.

```{r}
pacman::p_load(dplyr,
               lubridate,
               googlesheets4,
               gt,
               timesaveR)
```

```{r}
# tell googlesheets4 we don't want private
gs4_deauth()
```



```{r}
#| eval: true

# Create syllabus structure ####

# define negative %in%; don't end up using this I think
'%ni%' <- Negate("%in%")

# create tibble containing all weekly dates from first lecture until last
dates <- tibble(
  Datum = as.character(seq(ymd("2023-10-18"), ymd("2024-2-14"), by = "weeks")))

# create vector with dates of holidays
holidays <- c("2024-01-03",
              "2023-12-27" )

# remove holiday dates and add Woche, which lists the week number
syllabus <-
  dates |> 
  mutate(class = ifelse(Datum %in% holidays, "Vorlesungsfrei", "class")) |> 
  mutate(Woche = 1:length(Datum), .by = class, .before = Datum) |> 
  mutate(Woche = ifelse(Datum %in% holidays, "Vorlesungsfrei", Woche)) |> 
  select(-class)
```

```{r}
content <- 
  googlesheets4::read_sheet("https://docs.google.com/spreadsheets/d/1zg0Rb5S8p4oggjWxMU60zC8aFTJIblx3pAuI0SON4AY/edit?usp=sharing") |> 
  mutate(Woche = as.character(Woche),
         Thema = ifelse(is.na(topic_link), topic,
                        paste0(
                          "[",topic,"]",
                          "(","https://daniela-palleschi.github.io/ba-datenverarbeitung-wise23/",topic_link,")")), 
         Vorbereitung = ifelse(is.na(prepare_link), prepare,
                               paste0("[",prepare,"]","(",prepare_link,")"))) |> 
  select(Woche, Thema, Vorbereitung)
```

```{r}
left_join(
  syllabus, content, by = "Woche"
) |> 
  gt() |>
  sub_missing(columns = c(Woche, Thema, Vorbereitung), missing_text = "") |>
  cols_align(
    align = "center", 
    columns = c(Woche)
    ) |>
  cols_align(
    align = "left", 
    columns = c(Thema, Vorbereitung)
    ) |>
  tab_style(
    style = cell_borders(
      sides = "right",
      color = "#D3D3D3",
      style = "solid"
    ),
    locations = cells_body(
      columns = c(Thema, Vorbereitung)
    )
  ) |>
  fmt_markdown(
    columns = c(Thema, Vorbereitung)
  ) |>
  cols_width(
    Woche ~ px(150),
    Thema ~ px(400),
    Vorbereitung ~ px(300)
  ) 
```